Construction and predictive effect analysis of a nomogram model for predicting the progression-free survival of patients with positive Rictor protein in tissues of D2 + radical gastric adenocarcinoma resection / 肿瘤研究与临床

ABSTRACT

Objective:To construct a nomogram model for predicting the progression-free survival (PFS) of patients with positive Rictor protein tested in lesion tissues of D2 + radical gastric adenocarcinoma resection and to analyze its predictive value. Methods:The tissue samples of 1 366 gastric adenocarcinoma patients who underwent radical resection in Shanxi Province Cancer Hospital from May 2005 to December 2020 were retrospectively collected, and the Rictor protein expression was detected by using the immunohistochemical SP method. The 676 Rictor-positive cases were grouped in a 7∶3 ratio by simple randomization, including 496 cases in the training cohort and 180 cases in the validation cohort. The correlation of Rictor protein expression and other clinicopathological factors with PFS of Rictor-positive patients was analyzed by using a multifactorial Cox proportional risk model to determine the independent influencing factors of PFS. The nomogram for predicting the 3-year and 5-year PFS rates of patients with gastric adenocarcinoma was constructed based on the independent influencing factors of PFS. The constructed nomogram was bootstrapped with 1 000 resamplings for internal validation to test the accuracy of the prediction model. The internal and external predictive efficacy of the model was further assessed by calibration curves, area under the curve (AUC) of time-dependent receiver operating characteristic (ROC) and decision curve analysis (DCA). The nomogram model was applied to score the PFS of 496 cases in the training cohort, and the X-tile software was used to obtain the best cut-off value for the score. The overall cohort, training cohort and validation cohort cases were divided into low-risk group (≤ best cut-off value) and high-risk group (> best cut-off value) according to the best cut-off value, and the Kaplan-Meier method was used to analyze the difference in PFS between the low-risk group and high-risk group.Results:Multifactorial Cox regression analysis showed that gender, age, pT stage, number of positive lymph nodes, neural invasion, tumor longest diameter, omental invasion, Clavien-Dindo classification of postoperative complications, and CGA expression were independent influencing factors for PFS of the training cohort with Rictor-positive gastric adenocarcinoma. The nomogram for predicting the 3-year and 5-year PFS rates of patients with Rictor-positive gastric adenocarcinoma was constructed based on the above indicators. The calibration curve for internal validation and external validation showed good agreement between the prediction of nomogram and actual PFS. The time-ROC curve showed that the AUC of the internally validated and externally validated models for predicting the 3-year PFS rate was 0.834 (95% CI 0.746-0.823) and 0.799 (95% CI 0.699-0.868), and the AUC for predicting the 5-year PFS rate was 0.817 (95% CI 0.718-0.821) and 0.795 (95% CI 0.675-0.895). The C index of the model for overall prediction was 0.795 (95% CI 0.764-0.825), which was better than the 8th edition of American Joint Committee on Cancer (AJCC) TNM staging [0.693 (95% CI 0.662-0.723)]; the external validation DCA showed that the C index of the model for prediction was 0.769 (95% CI 0.718-0.821). The X-tile software analysis showed that the best cut-off value for the PFS score of the training cohort model was 265.08, with 457, 337 and 120 cases in the low-risk group and 219, 159 and 60 cases in the high-risk group for the overall cohort, training cohort and validation cohort, respectively. Kaplan-Meier survival analysis showed that the median PFS time was not reached in the low-risk group for the overall cohort, training cohort and validation cohort, and the median PFS time was 24, 24 and 28 months in the high-risk group, and there were statistical differences in PFS between the low-risk and high-risk groups for each cohort (all P < 0.001). Conclusions:For the first time, a nomogram model for PFS prediction in gastric adenocarcinoma patients with Rictor-positive expression is successfully constructed, which could better distinguish between patients with low-risk and high-risk of PFS. For high-risk patients with Rictor-positive gastric adenocarcinoma, in addition to controlling tumor metastasis and postoperative complications, attention should be paid to the targeted therapy for positive expression of Rictor.